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Session Restarted (Out of Memory)

Why This Alert Was Triggered​

Your session ran out of memory and was automatically killed and restarted. This happens when:

  • Your session tried to use more memory than was allocated to it.
  • Memory usage spiked suddenly, exceeding the limit before you could react
  • A process had a memory leak that gradually consumed all available memory
  • Multiple memory-intensive operations ran simultaneously

What This Means​

When a session is restarted:

  • Any changes not committed to Git and pushed to remote, or saved to disk are gone
  • In-progress processes were terminated
  • Your Python kernel, R session, or other runtime environments have been reset

Steps to Remedy​

Immediate Actions​

  1. Check what was lost:

    • Review your Git status to see if you have uncommitted changes: git status
    • Look for any auto-saved files or checkpoints
    • Check if your notebook or IDE has auto-recovery features

  2. Prevent recurrence before restarting work:

    • Review what you were running when the restart occurred
    • Identify memory-intensive operations that need optimisation

Longer-Term Solutions​

  1. Optimise memory usage:

    • Process data in smaller chunks instead of loading everything at once
    • Use memory-efficient data structures and algorithms
    • Stream data from disk rather than loading it all into memory
    • Delete large variables when you're done with them: del variable in Python, rm(variable) in R
    • Use generators or iterators for large datasets

  2. Request more memory:

    • Pause your current session
    • Modify the session resources to use a resource class with more memory, if available
    • Resume your session
    • See Resource Pools and Classes for more information

  3. Monitor memory usage:

    • Add memory profiling to your code
    • Use the High Memory Usage alert as an early warning system

  4. Save work frequently:

    • Commit and push changes to Git regularly
    • Save intermediate results to disk
    • Use checkpoint systems in long-running computations

Prevention​

  • Choose an appropriate resource class when starting sessions based on your expected workload
  • Test code with small datasets first to estimate memory requirements